Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application

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• 作者：Yadong Xu ; Marc L. Serre ; Jeanette Reyes ; William Vizuete
• 刊名：Environmental Science & Technology
• 出版年：2016
• 出版时间：April 19, 2016
• 年：2016
• 卷：50
• 期：8
• 页码：4393-4400
• 全文大小：457K
• 年卷期：0
• ISSN：1520-5851

文摘

To improve ozone exposure estimates for ambient concentrations at a national scale, we introduce our novel Regionalized Air Quality Model Performance (RAMP) approach to integrate chemical transport model (CTM) predictions with the available ozone observations using the Bayesian Maximum Entropy (BME) framework. The framework models the nonlinear and nonhomoscedastic relation between air pollution observations and CTM predictions and for the first time accounts for variability in CTM model performance. A validation analysis using only noncollocated data outside of a validation radius r_v was performed and the R² between observations and re-estimated values for two daily metrics, the daily maximum 8-h average (DM8A) and the daily 24-h average (D24A) ozone concentrations, were obtained with the OBS scenario using ozone observations only in contrast with the RAMP and a Constant Air Quality Model Performance (CAMP) scenarios. We show that, by accounting for the spatial and temporal variability in model performance, our novel RAMP approach is able to extract more information in terms of R² increase percentage, with over 12 times for the DM8A and over 3.5 times for the D24A ozone concentrations, from CTM predictions than the CAMP approach assuming that model performance does not change across space and time.